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Abstract Background Bayesian networks (BNs) have been widely used to estimate gene regulatory networks. Many BN methods have been developed to estimate networks from microarray data. However, two serious problems reduce the effectiveness of current BN methods. The first problem is that BN-based methods require huge computational time to estimate large-scale networks. The second is that the estimated network cannot have cyclic structures, even if the actual network has such structures. Results In this paper, we present a novel BN-based deterministic method with reduced computational time that allows cyclic structures. Our approach generates all the combinational triplets of genes, estimates networks of the triplets by BN, and unites the networks into a single network containing all genes. This method decreases the search space of predicting gene regulatory networks without degrading the solution accuracy compared with the greedy hill climbing (GHC) method. The order of computational time is the cube of number of genes. In addition, the network estimated by our method can include cyclic structures. Conclusions We verified the effectiveness of the proposed method for all known gene regulatory networks and their expression profiles. The results demonstrate that this approach can predict regulatory networks with reduced computational time without degrading the solution accuracy compared with the GHC method.

The purpose of this study is to apply a multidisciplinary optimization method for the conceptual designs of hypersonic experimental vehicles with advanced airbreathing engines. Using this method, we obtain the necessary vehicle sizes and optimal flight trajectories. The vehicle has a lifting body shape. First, we integrate analytical methods into the optimization problem, the solution of which yields the smallest gross weight of the vehicle. The results indicate a vehicle that is about 12 m in total length and 4-5 Mg in gross weight. This information allows us to determine the optimal vehicle configuration and flight trajectory for a highly feasible hypersonic vehicle. In addition, the trajectory optimizations enable the vehicle to glide back to a takeoff point without consuming propellant. This study confirms the effectiveness of the proposed analysis and optimization method.

Recent epigenetics research has demonstrated that chromatin conformation plays an important role in various aspects of gene regulation. Chromosome Conformation Capture (3C) technology makes it possible to analyze the spatial organization of chromatin in a cell. Several algorithms for three-dimensional reconstruction of chromatin structure from 3C experimental data have been proposed. Compared to other algorithms, ShRec3D, one of the most advanced algorithms, can reconstruct a chromatin model in the shortest time for high-resolution whole-genome experimental data. However, ShRec3D employs a graph shortest path algorithm, which introduces errors in the resulting model. We propose an improved algorithm that optimizes shortest path distances using a genetic algorithm approach. The proposed algorithm and ShRec3D were compared using in silico 3C experimental data. Compared to ShRec3D, the proposed algorithm demonstrated significant improvement relative to the similarity between the algorithm's output and the original model with a reasonable increase to calculation time. [ABSTRACT FROM AUTHOR]

Background: Comprehensively understanding the dynamics of biological systems is among the biggest current challenges in biology and medicine. To acquire this understanding, researchers have measured the time-series expression profiles of cell lines of various organisms. Biological technologies have also drastically improved, providing a huge amount of information with support from bioinformatics and systems biology. However, the transitions between the activation and inactivation of gene regulations, at the temporal resolution of single time points, are difficult to extract from time-course gene expression profiles. Results: Our proposed method reports the activation period of each gene regulation from gene expression profiles and a gene regulatory network. The correctness and effectiveness of the method were validated by analyzing the diauxic shift from glucose to lactose in Escherichia coli. The method completely detected the three periods of the shift; 1) consumption of glucose as nutrient source, 2) the period of seeking another nutrient source and 3) consumption of lactose as nutrient source. We then applied the method to mouse adipocyte differentiation data. Cell differentiation into adipocytes is known to involve two waves of the gene regulation cascade, and sub-waves are predicted. From the gene expression profiles of the cell differentiation process from ES to adipose cells (62 time points), our method acquired four periods; three periods covering the two known waves of the cascade, and a final period of gene regulations when the differentiation to adipocytes was completed. Conclusions: Our proposed method identifies the transitions of gene regulations from time-series gene expression profiles. Dynamic analyses are essential for deep understanding of biological systems and for identifying the causes of the onset of diseases such as diabetes and osteoporosis. The proposed method can greatly contribute to the progress of biology and medicine. [ABSTRACT FROM AUTHOR]

Background: Epigenetic factors associated with the development of autoimmune diseases are unclear. Monozygotic twin pairs discordant for positive antithyroglobulin autoantibodies (TgAb) are useful to examine the epigenetic factors because of their identical genetic background. This study aimed to clarify the discordant epigenetic differences affecting the development of TgAb. Methods: Subjects were selected from 257 Japanese monozygotic twins, recruited from the registry established by the Center for Twin Research at Osaka University. TgAb positive concordant (PC) pairs were 5.7% (four pairs) and 9.6% (18 pairs) of male and female pairs, respectively. TgAb discordant (DC) pairs were 11.4% (eight pairs) and 8.0% (15 pairs) of male and female pairs, respectively. TgAb negative concordant (NC) pairs were 78.6% (55 pairs) of male pairs and 74.3% (139 pairs) of female pairs. To perform stricter grouping, the cut-off value for positive TgAb was set to 50.0 IU/mL (TgAb negative: <28.0 IU/mL; TgAb positive: ≥50.0 IU/mL; TgAb borderline: ≥28.0 IU/mL and <50.0 IU/mL). Nineteen discordant (6 male and 13 female pairs) and 185 concordant pairs (48 male and 137 female pairs) for TgAb positivity were finally examined. DNA methylation levels of genomic DNA were evaluated using the Infinium HumanMethylation450 BeadChip Kit. Gene polymorphisms were also genotyped using the Omni5-4 BeadChip Kit to clarify genetic background specific for discordant twins. Results: No CpG sites were found with significant within-pair differences of methylation levels in TgAb DC pairs after correction for multiple comparisons. However, 155 polymorphisms specific for TgAb DC pairs were significantly different in genotype frequencies from those of concordant pairs, and none of them were located on the HLA region of chromosome 6. In TgAb DC pairs with some specific genotypes of these polymorphisms, four CpG sites were observed exhibiting significant within-pair differences in each DC pair, even after correction for multiple comparisons. Conclusions: This study found that the genetic background specific for TgAb DC twins who are susceptible to epigenetic changes are different from that specific for TgAb PC twins, and it clarified the genotype-based epigenetic differences in TgAb DC monozygotic twins. [ABSTRACT FROM AUTHOR]

Comprehensively understanding the dynamics of biological systems is one of the greatest challenges in biology. Vastly improved biological technologies have provided vast amounts of information that must be understood by bioinformatics and systems biology researchers. Gene regulations have been frequently modeled by ordinary differential equations or graphical models based on time-course gene expression profiles. The state-of-the-art computational approaches for analyzing gene regulations assume that their models are same throughout time-course experiments. However, these approaches cannot easily analyze transient changes at a time point, such as diauxic shift. We propose a score that analyzes the gene regulations at each time point. The score is based on the information gains of information criterion values. The method detects the shifts in gene regulatory networks (GRNs) during time-course experiments with single-time-point resolution. The effectiveness of the method is evaluated on the diauxic shift from glucose to lactose in Escherichia coli. Gene regulation shifts were detected at two time points: the first corresponding to the time at which the growth of E. coli ceased and the second corresponding to the end of the experiment, when the nutrient sources (glucose and lactose) had become exhausted. According to these results, the proposed score and method can appropriately detect the time of gene regulation shifts. The method based on the proposed score provides a new tool for analyzing dynamic biological systems. Because the score value indicates the strength of gene regulation at each time point in a gene expression profile, it can potentially infer hidden GRNs from time-course experiments. [ABSTRACT FROM AUTHOR]

The S-system model is one of the nonlinear differential equation models of gene regulatory networks, and it can describe various dynamics of the relationships among genes. If we successfully infer rigorous S-system model parameters that describe a target gene regulatory network, we can simulate gene expressions mathematically. However, the problem of finding an optimal S-system model parameter is too complex to be solved analytically. Thus, some heuristic search methods that offer approximate solutions are needed for reducing the computational time. In previous studies, several heuristic search methods such as Genetic Algorithms (GAs) have been applied to the parameter search of the S-system model. However, they have not achieved enough estimation accuracy. One of the conceivable reasons is that the mechanisms to escape local optima. We applied an Immune Algorithm (IA) to search for the S-system parameters. IA is also a heuristic search method, which is inspired by the biological mechanism of acquired immunity. Compared to GA, IA is able to search large solution space, thereby avoiding local optima, and have multiple candidates of the solutions. These features work well for searching the S-system model. Actually, our algorithm showed higher performance than GA for both simulation and real data analyses. [ABSTRACT FROM AUTHOR]

Background: With the advent of next-generation sequencers, the growing demands to map short DNA sequences to a genome have promoted the development of fast algorithms and tools. The tools commonly used today are based on either a hash table or the suffix array/Burrow-Wheeler transform. These algorithms are the best suited to finding the genome position of exactly matching short reads. However, they have limited capacity to handle the mismatches. To find n-mismatches, they requires O(2n) times the computation time of exact matches. Therefore, acceleration techniques are required. Results: We propose a hash-based method for genome mapping that reduces the number of hash references for finding mismatches without increasing the size of the hash table. The method regards DNA subsequences as words on Galois extension field GF(2²) and each word is encoded to a code word of a perfect Hamming code. The perfect Hamming code defines equivalence classes of DNA subsequences. Each equivalence class includes subsequence whose corresponding words on GF(2²) are encoded to a corresponding code word. The code word is used as a hash key to store these subsequences in a hash table. Specifically, it reduces by about 70% the number of hash keys necessary for searching the genome positions of all 2-mismatches of 21-base-long DNA subsequence. Conclusions: The paper shows perfect hamming code can reduce the number of hash references for hash-based genome mapping. As the computation time to calculate code words is far shorter than a hash reference, our method is effective to reduce the computation time to map short DNA sequences to genome. The amount of data that DNA sequencers generate continues to increase and more accurate genome mappings are required. Thus our method will be a key technology to develop faster genome mapping software. [ABSTRACT FROM AUTHOR]

With the advancement of genome research, it is becoming clear that genes are not distributed on the genome in random order. Clusters of genes distributed at localized genome positions have been reported in several eukaryotes. Various correlations have been observed between the expressions of genes in adjacent or nearby positions along the chromosomes depending on tissue type and developmental stage. Moreover, in several cases, their transcripts, which control epigenetic transcription via processes such as transcriptional interference and genomic imprinting, occur in clusters. It is reasonable that genomic regions that have similar mechanisms show similar expression patterns and that the characteristics of expression in the same genomic regions differ depending on tissue type and developmental stage. In this study, we analyzed gene expression patterns using the cap analysis gene expression (CAGE) method for exploring systematic views of the mouse transcriptome. Counting the number of mapped CAGE tags for fixed-length regions allowed us to determine genomic expression levels. These expression levels were normalized, quantified, and converted into four types of descriptors, allowing the expression patterns along the genome to be represented by character strings. We analyzed them using dynamic programming in the same manner as for sequence analysis. We have developed a novel algorithm that provides a novel view of the genome from the perspective of genomic positional expression. In a similarity search of expression patterns across chromosomes and tissues, we found regions that had clusters of genes that showed expression patterns similar to each other depending on tissue type. Our results suggest the possibility that the regions that have sense-antisense transcription show similar expression patterns between forward and reverse strands. [ABSTRACT FROM AUTHOR]

In this paper, we propose a neural network algorithm that uses the expanded maximum neuron model to solve the channel assignment problem of cellular radio networks, which is an NP-complete combinatorial optimization problem. The channel assignment problem demands minimizing the total interference between the assigned channels needed to satisfy all of the communication needs. The proposed expanded maximum neuron model selects multiple neurons in descending order from the neuron inputs in each neuron group. As a result, the constraints will always be satisfied for the channel assignment problem. To improve the accuracy of the solution, neuron fixing, which is a heuristic technique used in the binary neuron model, a hill-climbing term, a shaking term, and an Omega function are introduced. The effectiveness of these additions to the expanded maximum neuron model algorithm is demonstrated. Simulations of benchmark problems demonstrate the superior performance of the proposed algorithm over conventional algorithms in finding the solution. © 2000 Scripta Technica, Electron Comm Jpn Pt 3, 83(11): 11–19, 2000 [ABSTRACT FROM AUTHOR]

Abstract: Background: Identifying the differences between gene regulatory networks under varying biological conditions or external stimuli is an important challenge in systems biology. Several methods have been developed to reverse-engineer a cellular system, called a gene regulatory network, from gene expression profiles in order to understand transcriptomic behavior under various conditions of interest. Conventional methods infer the gene regulatory network independently from each of the multiple gene expression profiles under varying conditions to find the important regulatory relations for understanding cellular behavior. However, the inferred networks with conventional methods include a large number of misleading relations, and the accuracy of the inference is low. This is because conventional methods do not consider other related conditions, and the results of conventional methods include considerable noise due to the limited number of observation points in each expression profile of interest. Results: We propose a more accurate method for estimating key gene regulatory networks for understanding cellular behavior under various conditions. Our method utilizes multiple gene expression profiles that compose a tree structure under varying conditions. The root represents the original cellular state, and the leaves represent the changed cellular states under various conditions. By using this tree-structured gene expression profiles, our method more powerfully estimates the networks that are key to understanding the cellular behavior of interest under varying conditions. Conclusion: We confirmed that the proposed method in cell differentiation was more rigorous than the conventional method. The results show that our assumptions as to which relations are unimportant for understanding the differences of cellular states in cell differentiation are appropriate, and that our method can infer more accurately the core networks of the cell types. [Copyright &y& Elsevier]

Background/objectives: The dynamic interaction between genomic DNA, epigenetic modifications, and phenotypic traits was examined in identical twins. Environmental perturbations can induce epigenetic changes in DNA methylation, influencing gene expression and phenotypes. Although DNA methylation mediates gene-environment correlations, the quantitative effects of external factors on DNA methylation remain underexplored. This study aimed to quantify these effects using a novel approach., Methods: A cohort study was conducted on healthy monozygotic twins to evaluate the influence of environmental stimuli on DNA methylation. We developed the Environmental Factor Index (EFI) to identify methylation sites showing statistically significant changes in response to environmental stimuli. We analyzed the identified sites for associations with disorders, DNA methylation markers, and CpG islands., Results: The EFI identified methylation sites that exhibited significant associations with genes linked to various disorders, particularly cancer. These sites were overrepresented on CpG islands compared to other genomic features, highlighting their regulatory importance., Conclusions: The EFI is a valuable tool for understanding the molecular mechanisms underlying disease pathogenesis. It provides insights into the development of preventive and therapeutic strategies and offers a new perspective on the role of environmental factors in epigenetic regulation.

A vast amount of metagenomic data has been obtained by extracting multiple genomes simultaneously from microbial communities, including genomes from uncultivable microbes. By analyzing these metagenomic data, novel microbes are discovered and new microbial functions are elucidated. The first step in analyzing these data is sequenced-read classification into reference genomes from which each read can be derived. The Naïve Bayes Classifier is a method for this classification. To identify the derivation of the reads, this method calculates a score based on the occurrence of a DNA sequence motif in each reference genome. However, large differences in the sizes of the reference genomes can bias the scoring of the reads. This bias might cause erroneous classification and decrease the classification accuracy. To address this issue, we have updated the Naïve Bayes Classifier method using multiple sets of occurrence profiles for each reference genome by normalizing the genome sizes, dividing each genome sequence into a set of subsequences of similar length and generating profiles for each subsequence. This multiple profile strategy improves the accuracy of the results generated by the Naïve Bayes Classifier method for simulated and Sargasso Sea datasets.

Efficient execution of data-intensive workflows has been playing an important role in bioinformatics as the amount of data has been rapidly increasing. The execution of such workflows must take into account the volume and pattern of communication. When orchestrating data-centric workflows, a centralized workflow engine can become a bottleneck to performance. To cope with the bottleneck, a hybrid approach with choreography for data management of workflows is proposed. However, when a workflow includes many repetitive operations, the approach might not gain good performance because of the overheads of its additional mechanism. This paper presents and evaluates an improvement of the hybrid approach for managing a large amount of data. The performance of the proposed method is demonstrated by measuring execution times of example workflows.

Using deep sequencing (deepCAGE), the FANTOM4 study measured the genome-wide dynamics of transcription-start-site usage in the human monocytic cell line THP-1 throughout a time course of growth arrest and differentiation. Modeling the expression dynamics in terms of predicted cis-regulatory sites, we identified the key transcription regulators, their time-dependent activities and target genes. Systematic siRNA knockdown of 52 transcription factors confirmed the roles of individual factors in the regulatory network. Our results indicate that cellular states are constrained by complex networks involving both positive and negative regulatory interactions among substantial numbers of transcription factors and that no single transcription factor is both necessary and sufficient to drive the differentiation process.

We present a new method to describe tissue-specific function that leverages the advantage of the Cap Analysis of Gene Expression (CAGE) data. The CAGE expression data represent the number of mRNAs of each gene in a sample. The feature enables us to compare or add the expression amount of genes in the sample. As usual methods compared the gene expression values among tissues for each gene respectively and ruled out to compare them among genes, they have not exploited the feature to reveal tissue specificity. To utilize the feature, we used Gene Ontology terms (GO-terms) as unit to sum up the expression values and described specificities of tissues by them. We regard GO-terms as events that occur in the tissue according to probabilities that are defined by means of the CAGE. Our method is applied to mouse CAGE data on 22 tissues. Among them, we show the results of molecular functions and cellular components on liver. We also show the most expressed genes in liver to compare with our method. The results agree well with well-known specific functions such as amino acid metabolisms of liver. Moreover, the difference of inter-cellular junction among liver, lung, heart, muscle and prostate gland are apparently observed. The results of our method provide researchers a clue to the further research of the tissue roles and the deeper functions of the tissue-specific genes. All the results and supplementary materials are available via our web site.

The international FANTOM consortium aims to produce a comprehensive picture of the mammalian transcriptome, based upon an extensive cDNA collection and functional annotation of full-length enriched cDNAs. The previous dataset, FANTOM2, comprised 60,770 full-length enriched cDNAs. Functional annotation revealed that this cDNA dataset contained only about half of the estimated number of mouse protein-coding genes, indicating that a number of cDNAs still remained to be collected and identified. To pursue the complete gene catalog that covers all predicted mouse genes, cloning and sequencing of full-length enriched cDNAs has been continued since FANTOM2. In FANTOM3, 42,031 newly isolated cDNAs were subjected to functional annotation, and the annotation of 4,347 FANTOM2 cDNAs was updated. To accomplish accurate functional annotation, we improved our automated annotation pipeline by introducing new coding sequence prediction programs and developed a Web-based annotation interface for simplifying the annotation procedures to reduce manual annotation errors. Automated coding sequence and function prediction was followed with manual curation and review by expert curators. A total of 102,801 full-length enriched mouse cDNAs were annotated. Out of 102,801 transcripts, 56,722 were functionally annotated as protein coding (including partial or truncated transcripts), providing to our knowledge the greatest current coverage of the mouse proteome by full-length cDNAs. The total number of distinct non-protein-coding transcripts increased to 34,030. The FANTOM3 annotation system, consisting of automated computational prediction, manual curation, and final expert curation, facilitated the comprehensive characterization of the mouse transcriptome, and could be applied to the transcriptomes of other species., Competing Interests: Competing interests. The authors have declared that no competing interests exist.

Recently, gene expression data under various conditions have largely been obtained by the utilization of the DNA microarrays and oligonucleotide arrays. There have been emerging demands to analyze the function of genes from the gene expression profiles. For clustering genes from their expression profiles, hierarchical clustering has been widely used. The clustering method represents the relationships of genes as a tree structure by connecting genes using their similarity scores based on the Pearson correlation coefficient. But the clustering method is sensitive to experimental noise. To cope with the problem, we propose another type of clustering method (the p-quasi complete linkage clustering). We apply this method to the gene expression data of yeast cell-cycles and human lung cancer. The effectiveness of our method is demonstrated by comparing clustering results with other methods.